Overview

Dataset statistics

Number of variables36
Number of observations10000
Missing cells112499
Missing cells (%)31.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 MiB
Average record size in memory288.0 B

Variable types

Numeric15
Categorical14
Unsupported7

Alerts

R has constant value "0.0"Constant
UPN has a high cardinality: 8631 distinct valuesHigh cardinality
EntryDate has a high cardinality: 410 distinct valuesHigh cardinality
/ is highly overall correlated with \High correlation
\ is highly overall correlated with /High correlation
B is highly overall correlated with G and 1 other fieldsHigh correlation
G is highly overall correlated with BHigh correlation
D is highly overall correlated with BHigh correlation
EnrolStatus is highly imbalanced (77.2%)Imbalance
J is highly imbalanced (79.5%)Imbalance
T is highly imbalanced (76.3%)Imbalance
N is highly imbalanced (96.1%)Imbalance
Surname has 10000 (100.0%) missing valuesMissing
Forename has 10000 (100.0%) missing valuesMissing
Middlenames has 10000 (100.0%) missing valuesMissing
PreferredSurname has 10000 (100.0%) missing valuesMissing
FormerSurname has 10000 (100.0%) missing valuesMissing
DoB has 10000 (100.0%) missing valuesMissing
B has 2582 (25.8%) missing valuesMissing
J has 2533 (25.3%) missing valuesMissing
L has 1066 (10.7%) missing valuesMissing
P has 2519 (25.2%) missing valuesMissing
V has 2397 (24.0%) missing valuesMissing
W has 2559 (25.6%) missing valuesMissing
C has 2200 (22.0%) missing valuesMissing
E has 2556 (25.6%) missing valuesMissing
H has 2627 (26.3%) missing valuesMissing
I has 200 (2.0%) missing valuesMissing
M has 1916 (19.2%) missing valuesMissing
R has 2619 (26.2%) missing valuesMissing
S has 10000 (100.0%) missing valuesMissing
T has 2625 (26.2%) missing valuesMissing
G has 2589 (25.9%) missing valuesMissing
N has 2620 (26.2%) missing valuesMissing
O has 1865 (18.6%) missing valuesMissing
U has 2473 (24.7%) missing valuesMissing
D has 2611 (26.1%) missing valuesMissing
X has 1938 (19.4%) missing valuesMissing
UPN is uniformly distributedUniform
Surname is an unsupported type, check if it needs cleaning or further analysisUnsupported
Forename is an unsupported type, check if it needs cleaning or further analysisUnsupported
Middlenames is an unsupported type, check if it needs cleaning or further analysisUnsupported
PreferredSurname is an unsupported type, check if it needs cleaning or further analysisUnsupported
FormerSurname is an unsupported type, check if it needs cleaning or further analysisUnsupported
DoB is an unsupported type, check if it needs cleaning or further analysisUnsupported
S is an unsupported type, check if it needs cleaning or further analysisUnsupported
B has 1267 (12.7%) zerosZeros
L has 602 (6.0%) zerosZeros
V has 2420 (24.2%) zerosZeros
C has 684 (6.8%) zerosZeros
I has 347 (3.5%) zerosZeros
M has 1858 (18.6%) zerosZeros
G has 1875 (18.8%) zerosZeros
O has 464 (4.6%) zerosZeros
D has 534 (5.3%) zerosZeros
X has 1424 (14.2%) zerosZeros
Y has 951 (9.5%) zerosZeros

Reproduction

Analysis started2023-06-26 14:03:41.503169
Analysis finished2023-06-26 14:04:18.054268
Duration36.55 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Estab
Real number (ℝ)

Distinct9608
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190317.17
Minimum61847
Maximum293877
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:04:18.165862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum61847
5-th percentile131123.1
Q1166206
median190689.5
Q3214605.25
95-th percentile248955.1
Maximum293877
Range232030
Interquartile range (IQR)48399.25

Descriptive statistics

Standard deviation35796.284
Coefficient of variation (CV)0.18808751
Kurtosis-0.078718607
Mean190317.17
Median Absolute Deviation (MAD)24223.5
Skewness-0.057797107
Sum1.9031717 × 109
Variance1.2813739 × 109
MonotonicityNot monotonic
2023-06-26T15:04:18.343722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
181708 4
 
< 0.1%
181358 3
 
< 0.1%
169756 3
 
< 0.1%
200077 3
 
< 0.1%
208577 3
 
< 0.1%
171286 3
 
< 0.1%
170570 3
 
< 0.1%
184233 3
 
< 0.1%
195006 3
 
< 0.1%
241707 3
 
< 0.1%
Other values (9598) 9969
99.7%
ValueCountFrequency (%)
61847 1
< 0.1%
65531 1
< 0.1%
65650 1
< 0.1%
66628 1
< 0.1%
68607 1
< 0.1%
69241 1
< 0.1%
71326 1
< 0.1%
73043 1
< 0.1%
73785 1
< 0.1%
77532 1
< 0.1%
ValueCountFrequency (%)
293877 1
< 0.1%
293213 1
< 0.1%
292213 1
< 0.1%
292181 1
< 0.1%
291840 1
< 0.1%
291746 1
< 0.1%
291163 1
< 0.1%
290673 1
< 0.1%
289734 1
< 0.1%
289086 1
< 0.1%

UPN
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct8631
Distinct (%)86.3%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
28280060-6d12-44fb-9a57-62c6edb47e02
 
5
4ff87bd2-c25a-47fd-8ec1-81e03ae4dd80
 
4
2fd191f6-dd28-4161-88f9-075bb08e72aa
 
4
4ffc0010-11cd-4374-b5bd-a5f6f84cd994
 
4
d9efb153-6eaf-426f-a9e3-dd1e93ca2ded
 
4
Other values (8626)
9979 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters360000
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7414 ?
Unique (%)74.1%

Sample

1st row819c5d8d-a652-4a62-9eac-9fe483ddbca8
2nd row6318ab5a-2d2c-42ce-ab68-0945162d683d
3rd roweb1edd42-97c1-4a8d-b546-f49372eabdb8
4th row3064a34b-8961-4ac6-8540-a346ee12fb8b
5th rowde67351f-dd82-428f-8095-ba0f5a3ac738

Common Values

ValueCountFrequency (%)
28280060-6d12-44fb-9a57-62c6edb47e02 5
 
0.1%
4ff87bd2-c25a-47fd-8ec1-81e03ae4dd80 4
 
< 0.1%
2fd191f6-dd28-4161-88f9-075bb08e72aa 4
 
< 0.1%
4ffc0010-11cd-4374-b5bd-a5f6f84cd994 4
 
< 0.1%
d9efb153-6eaf-426f-a9e3-dd1e93ca2ded 4
 
< 0.1%
7f60f576-9654-4a5e-b624-577f8e9f9e4a 4
 
< 0.1%
b56226ca-5660-47cb-bc71-a3950ee90627 4
 
< 0.1%
e75c109e-08f4-4ded-bc53-61da302dad8d 4
 
< 0.1%
47be114c-a323-43ec-9ab9-0d2fc7c0083a 4
 
< 0.1%
ecc3ccb8-cf6c-4d6b-ac90-ab889b64b889 3
 
< 0.1%
Other values (8621) 9960
99.6%

Length

2023-06-26T15:04:18.486182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
28280060-6d12-44fb-9a57-62c6edb47e02 5
 
< 0.1%
2fd191f6-dd28-4161-88f9-075bb08e72aa 4
 
< 0.1%
4ffc0010-11cd-4374-b5bd-a5f6f84cd994 4
 
< 0.1%
d9efb153-6eaf-426f-a9e3-dd1e93ca2ded 4
 
< 0.1%
7f60f576-9654-4a5e-b624-577f8e9f9e4a 4
 
< 0.1%
b56226ca-5660-47cb-bc71-a3950ee90627 4
 
< 0.1%
e75c109e-08f4-4ded-bc53-61da302dad8d 4
 
< 0.1%
47be114c-a323-43ec-9ab9-0d2fc7c0083a 4
 
< 0.1%
4ff87bd2-c25a-47fd-8ec1-81e03ae4dd80 4
 
< 0.1%
1f009a25-d760-4e17-8468-14a43dbfb850 3
 
< 0.1%
Other values (8621) 9960
99.6%

Most occurring characters

ValueCountFrequency (%)
- 40000
 
11.1%
4 28806
 
8.0%
9 21380
 
5.9%
8 21338
 
5.9%
a 21172
 
5.9%
b 21053
 
5.8%
d 19018
 
5.3%
1 18877
 
5.2%
3 18793
 
5.2%
0 18781
 
5.2%
Other values (7) 130782
36.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202760
56.3%
Lowercase Letter 117240
32.6%
Dash Punctuation 40000
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 28806
14.2%
9 21380
10.5%
8 21338
10.5%
1 18877
9.3%
3 18793
9.3%
0 18781
9.3%
7 18772
9.3%
5 18766
9.3%
6 18765
9.3%
2 18482
9.1%
Lowercase Letter
ValueCountFrequency (%)
a 21172
18.1%
b 21053
18.0%
d 19018
16.2%
f 18689
15.9%
e 18671
15.9%
c 18637
15.9%
Dash Punctuation
ValueCountFrequency (%)
- 40000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 242760
67.4%
Latin 117240
32.6%

Most frequent character per script

Common
ValueCountFrequency (%)
- 40000
16.5%
4 28806
11.9%
9 21380
8.8%
8 21338
8.8%
1 18877
7.8%
3 18793
7.7%
0 18781
7.7%
7 18772
7.7%
5 18766
7.7%
6 18765
7.7%
Latin
ValueCountFrequency (%)
a 21172
18.1%
b 21053
18.0%
d 19018
16.2%
f 18689
15.9%
e 18671
15.9%
c 18637
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 360000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 40000
 
11.1%
4 28806
 
8.0%
9 21380
 
5.9%
8 21338
 
5.9%
a 21172
 
5.9%
b 21053
 
5.8%
d 19018
 
5.3%
1 18877
 
5.2%
3 18793
 
5.2%
0 18781
 
5.2%
Other values (7) 130782
36.3%

Surname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Forename
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Middlenames
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

PreferredSurname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

FormerSurname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
M
5079 
F
4921 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowM
4th rowM
5th rowF

Common Values

ValueCountFrequency (%)
M 5079
50.8%
F 4921
49.2%

Length

2023-06-26T15:04:18.599502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:04:18.707615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
m 5079
50.8%
f 4921
49.2%

Most occurring characters

ValueCountFrequency (%)
M 5079
50.8%
F 4921
49.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 5079
50.8%
F 4921
49.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 10000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 5079
50.8%
F 4921
49.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 5079
50.8%
F 4921
49.2%

DoB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

EnrolStatus
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
C
9077 
Leaver
909 
M
 
8
S
 
6

Length

Max length6
Median length1
Mean length1.4545
Min length1

Characters and Unicode

Total characters14545
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLeaver
2nd rowC
3rd rowC
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C 9077
90.8%
Leaver 909
 
9.1%
M 8
 
0.1%
S 6
 
0.1%

Length

2023-06-26T15:04:18.804377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:04:18.922480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
c 9077
90.8%
leaver 909
 
9.1%
m 8
 
0.1%
s 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
C 9077
62.4%
e 1818
 
12.5%
L 909
 
6.2%
a 909
 
6.2%
v 909
 
6.2%
r 909
 
6.2%
M 8
 
0.1%
S 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10000
68.8%
Lowercase Letter 4545
31.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 9077
90.8%
L 909
 
9.1%
M 8
 
0.1%
S 6
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 1818
40.0%
a 909
20.0%
v 909
20.0%
r 909
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14545
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 9077
62.4%
e 1818
 
12.5%
L 909
 
6.2%
a 909
 
6.2%
v 909
 
6.2%
r 909
 
6.2%
M 8
 
0.1%
S 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14545
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 9077
62.4%
e 1818
 
12.5%
L 909
 
6.2%
a 909
 
6.2%
v 909
 
6.2%
r 909
 
6.2%
M 8
 
0.1%
S 6
 
< 0.1%

EntryDate
Categorical

Distinct410
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2021-09-03 00:00:00
1124 
2019-09-04 00:00:00
1112 
2020-09-03 00:00:00
1055 
2017-09-06 00:00:00
879 
2018-09-07 00:00:00
622 
Other values (405)
5208 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters190000
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique190 ?
Unique (%)1.9%

Sample

1st row2019-09-04 00:00:00
2nd row2021-09-03 00:00:00
3rd row2019-09-04 00:00:00
4th row2021-09-03 00:00:00
5th row2017-09-06 00:00:00

Common Values

ValueCountFrequency (%)
2021-09-03 00:00:00 1124
 
11.2%
2019-09-04 00:00:00 1112
 
11.1%
2020-09-03 00:00:00 1055
 
10.5%
2017-09-06 00:00:00 879
 
8.8%
2018-09-07 00:00:00 622
 
6.2%
2018-09-06 00:00:00 440
 
4.4%
2021-09-02 00:00:00 400
 
4.0%
2018-09-05 00:00:00 392
 
3.9%
2020-09-02 00:00:00 364
 
3.6%
2020-09-01 00:00:00 321
 
3.2%
Other values (400) 3291
32.9%

Length

2023-06-26T15:04:19.034585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00 10000
50.0%
2021-09-03 1124
 
5.6%
2019-09-04 1112
 
5.6%
2020-09-03 1055
 
5.3%
2017-09-06 879
 
4.4%
2018-09-07 622
 
3.1%
2018-09-06 440
 
2.2%
2021-09-02 400
 
2.0%
2018-09-05 392
 
2.0%
2020-09-02 364
 
1.8%
Other values (401) 3612
 
18.1%

Most occurring characters

ValueCountFrequency (%)
0 91298
48.1%
- 20000
 
10.5%
: 20000
 
10.5%
2 16181
 
8.5%
9 11129
 
5.9%
10000
 
5.3%
1 9638
 
5.1%
3 2788
 
1.5%
7 2282
 
1.2%
8 2168
 
1.1%
Other values (3) 4516
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140000
73.7%
Dash Punctuation 20000
 
10.5%
Other Punctuation 20000
 
10.5%
Space Separator 10000
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 91298
65.2%
2 16181
 
11.6%
9 11129
 
7.9%
1 9638
 
6.9%
3 2788
 
2.0%
7 2282
 
1.6%
8 2168
 
1.5%
4 1869
 
1.3%
6 1666
 
1.2%
5 981
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 20000
100.0%
Other Punctuation
ValueCountFrequency (%)
: 20000
100.0%
Space Separator
ValueCountFrequency (%)
10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 190000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 91298
48.1%
- 20000
 
10.5%
: 20000
 
10.5%
2 16181
 
8.5%
9 11129
 
5.9%
10000
 
5.3%
1 9638
 
5.1%
3 2788
 
1.5%
7 2282
 
1.2%
8 2168
 
1.1%
Other values (3) 4516
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 190000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 91298
48.1%
- 20000
 
10.5%
: 20000
 
10.5%
2 16181
 
8.5%
9 11129
 
5.9%
10000
 
5.3%
1 9638
 
5.1%
3 2788
 
1.5%
7 2282
 
1.2%
8 2168
 
1.1%
Other values (3) 4516
 
2.4%

NCyearActual
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
9
2049 
7
2045 
10
2007 
8
1946 
11
1603 

Length

Max length6
Median length1
Mean length1.536
Min length1

Characters and Unicode

Total characters15360
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7
2nd row7
3rd row7
4th row9
5th row7

Common Values

ValueCountFrequency (%)
9 2049
20.5%
7 2045
20.4%
10 2007
20.1%
8 1946
19.5%
11 1603
16.0%
Leaver 350
 
3.5%

Length

2023-06-26T15:04:19.157238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:04:19.297590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
9 2049
20.5%
7 2045
20.4%
10 2007
20.1%
8 1946
19.5%
11 1603
16.0%
leaver 350
 
3.5%

Most occurring characters

ValueCountFrequency (%)
1 5213
33.9%
9 2049
 
13.3%
7 2045
 
13.3%
0 2007
 
13.1%
8 1946
 
12.7%
e 700
 
4.6%
L 350
 
2.3%
a 350
 
2.3%
v 350
 
2.3%
r 350
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13260
86.3%
Lowercase Letter 1750
 
11.4%
Uppercase Letter 350
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5213
39.3%
9 2049
 
15.5%
7 2045
 
15.4%
0 2007
 
15.1%
8 1946
 
14.7%
Lowercase Letter
ValueCountFrequency (%)
e 700
40.0%
a 350
20.0%
v 350
20.0%
r 350
20.0%
Uppercase Letter
ValueCountFrequency (%)
L 350
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13260
86.3%
Latin 2100
 
13.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5213
39.3%
9 2049
 
15.5%
7 2045
 
15.4%
0 2007
 
15.1%
8 1946
 
14.7%
Latin
ValueCountFrequency (%)
e 700
33.3%
L 350
16.7%
a 350
16.7%
v 350
16.7%
r 350
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5213
33.9%
9 2049
 
13.3%
7 2045
 
13.3%
0 2007
 
13.1%
8 1946
 
12.7%
e 700
 
4.6%
L 350
 
2.3%
a 350
 
2.3%
v 350
 
2.3%
r 350
 
2.3%

TermlySessionsPossible
Real number (ℝ)

Distinct57
Distinct (%)0.6%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean116.27271
Minimum63
Maximum128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:04:19.447937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum63
5-th percentile99
Q1111
median118
Q3123
95-th percentile127
Maximum128
Range65
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.9439173
Coefficient of variation (CV)0.076921896
Kurtosis1.1180331
Mean116.27271
Median Absolute Deviation (MAD)6
Skewness-1.0561247
Sum1162262
Variance79.993656
MonotonicityNot monotonic
2023-06-26T15:04:19.603773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126 557
 
5.6%
122 538
 
5.4%
125 522
 
5.2%
127 522
 
5.2%
123 514
 
5.1%
124 508
 
5.1%
121 498
 
5.0%
119 461
 
4.6%
120 446
 
4.5%
118 435
 
4.3%
Other values (47) 4995
50.0%
ValueCountFrequency (%)
63 1
 
< 0.1%
73 2
< 0.1%
74 1
 
< 0.1%
75 1
 
< 0.1%
76 2
< 0.1%
77 1
 
< 0.1%
78 3
< 0.1%
79 4
< 0.1%
80 3
< 0.1%
81 3
< 0.1%
ValueCountFrequency (%)
128 272
2.7%
127 522
5.2%
126 557
5.6%
125 522
5.2%
124 508
5.1%
123 514
5.1%
122 538
5.4%
121 498
5.0%
120 446
4.5%
119 461
4.6%

/
Real number (ℝ)

Distinct93
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.211
Minimum4
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:04:19.752151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile30
Q143
median51
Q360
95-th percentile72
Maximum100
Range96
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.938888
Coefficient of variation (CV)0.25265837
Kurtosis0.023861503
Mean51.211
Median Absolute Deviation (MAD)9
Skewness0.013446531
Sum512110
Variance167.41482
MonotonicityNot monotonic
2023-06-26T15:04:19.890323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 315
 
3.1%
49 308
 
3.1%
55 308
 
3.1%
51 305
 
3.0%
54 303
 
3.0%
48 303
 
3.0%
52 302
 
3.0%
46 301
 
3.0%
53 298
 
3.0%
57 295
 
2.9%
Other values (83) 6962
69.6%
ValueCountFrequency (%)
4 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
11 2
 
< 0.1%
12 3
< 0.1%
13 3
< 0.1%
14 2
 
< 0.1%
15 7
0.1%
ValueCountFrequency (%)
100 3
< 0.1%
99 1
 
< 0.1%
97 1
 
< 0.1%
96 2
 
< 0.1%
94 2
 
< 0.1%
93 1
 
< 0.1%
92 2
 
< 0.1%
91 4
< 0.1%
90 5
0.1%
89 4
< 0.1%

\
Real number (ℝ)

Distinct59
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.2222
Minimum3
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:04:20.020346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile32
Q145
median52
Q358
95-th percentile62
Maximum63
Range60
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.659563
Coefficient of variation (CV)0.19233652
Kurtosis0.77505467
Mean50.2222
Median Absolute Deviation (MAD)6
Skewness-0.97522663
Sum502222
Variance93.307158
MonotonicityNot monotonic
2023-06-26T15:04:20.151358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61 519
 
5.2%
62 482
 
4.8%
60 481
 
4.8%
57 479
 
4.8%
59 469
 
4.7%
58 449
 
4.5%
56 446
 
4.5%
55 442
 
4.4%
54 433
 
4.3%
51 416
 
4.2%
Other values (49) 5384
53.8%
ValueCountFrequency (%)
3 1
 
< 0.1%
4 1
 
< 0.1%
6 2
 
< 0.1%
8 3
< 0.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
11 2
 
< 0.1%
12 1
 
< 0.1%
13 6
0.1%
14 3
< 0.1%
ValueCountFrequency (%)
63 268
2.7%
62 482
4.8%
61 519
5.2%
60 481
4.8%
59 469
4.7%
58 449
4.5%
57 479
4.8%
56 446
4.5%
55 442
4.4%
54 433
4.3%

B
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)0.1%
Missing2582
Missing (%)25.8%
Infinite0
Infinite (%)0.0%
Mean1.712591
Minimum0
Maximum7
Zeros1267
Zeros (%)12.7%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:04:20.260273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2550689
Coefficient of variation (CV)0.73284804
Kurtosis-0.049708528
Mean1.712591
Median Absolute Deviation (MAD)1
Skewness0.57954382
Sum12704
Variance1.5751981
MonotonicityNot monotonic
2023-06-26T15:04:20.356983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 2336
23.4%
2 1933
19.3%
0 1267
12.7%
3 1217
12.2%
4 502
 
5.0%
5 137
 
1.4%
6 24
 
0.2%
7 2
 
< 0.1%
(Missing) 2582
25.8%
ValueCountFrequency (%)
0 1267
12.7%
1 2336
23.4%
2 1933
19.3%
3 1217
12.2%
4 502
 
5.0%
5 137
 
1.4%
6 24
 
0.2%
7 2
 
< 0.1%
ValueCountFrequency (%)
7 2
 
< 0.1%
6 24
 
0.2%
5 137
 
1.4%
4 502
 
5.0%
3 1217
12.2%
2 1933
19.3%
1 2336
23.4%
0 1267
12.7%

J
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing2533
Missing (%)25.3%
Memory size78.2 KiB
0.0
7227 
1.0
 
240

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters22401
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7227
72.3%
1.0 240
 
2.4%
(Missing) 2533
 
25.3%

Length

2023-06-26T15:04:20.486382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:04:20.610373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7227
96.8%
1.0 240
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 14694
65.6%
. 7467
33.3%
1 240
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14934
66.7%
Other Punctuation 7467
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14694
98.4%
1 240
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 7467
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22401
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14694
65.6%
. 7467
33.3%
1 240
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22401
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14694
65.6%
. 7467
33.3%
1 240
 
1.1%

L
Real number (ℝ)

MISSING  ZEROS 

Distinct26
Distinct (%)0.3%
Missing1066
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean4.8979181
Minimum0
Maximum25
Zeros602
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:04:20.707530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile12
Maximum25
Range25
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.7629719
Coefficient of variation (CV)0.76827988
Kurtosis1.0966142
Mean4.8979181
Median Absolute Deviation (MAD)2
Skewness1.030957
Sum43758
Variance14.159958
MonotonicityNot monotonic
2023-06-26T15:04:20.822574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 1168
11.7%
2 1067
10.7%
3 974
9.7%
4 971
9.7%
5 867
8.7%
6 693
6.9%
7 625
6.2%
0 602
6.0%
8 476
4.8%
9 422
 
4.2%
Other values (16) 1069
10.7%
(Missing) 1066
10.7%
ValueCountFrequency (%)
0 602
6.0%
1 1168
11.7%
2 1067
10.7%
3 974
9.7%
4 971
9.7%
5 867
8.7%
6 693
6.9%
7 625
6.2%
8 476
4.8%
9 422
 
4.2%
ValueCountFrequency (%)
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 3
 
< 0.1%
21 6
 
0.1%
20 5
 
0.1%
19 17
 
0.2%
18 19
0.2%
17 28
0.3%
16 44
0.4%

P
Categorical

Distinct5
Distinct (%)0.1%
Missing2519
Missing (%)25.2%
Memory size78.2 KiB
1.0
3536 
0.0
2997 
2.0
846 
3.0
 
98
4.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters22443
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 3536
35.4%
0.0 2997
30.0%
2.0 846
 
8.5%
3.0 98
 
1.0%
4.0 4
 
< 0.1%
(Missing) 2519
25.2%

Length

2023-06-26T15:04:20.940438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:04:21.067235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3536
47.3%
0.0 2997
40.1%
2.0 846
 
11.3%
3.0 98
 
1.3%
4.0 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 10478
46.7%
. 7481
33.3%
1 3536
 
15.8%
2 846
 
3.8%
3 98
 
0.4%
4 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14962
66.7%
Other Punctuation 7481
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10478
70.0%
1 3536
 
23.6%
2 846
 
5.7%
3 98
 
0.7%
4 4
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 7481
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22443
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10478
46.7%
. 7481
33.3%
1 3536
 
15.8%
2 846
 
3.8%
3 98
 
0.4%
4 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22443
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10478
46.7%
. 7481
33.3%
1 3536
 
15.8%
2 846
 
3.8%
3 98
 
0.4%
4 4
 
< 0.1%

V
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)0.1%
Missing2397
Missing (%)24.0%
Infinite0
Infinite (%)0.0%
Mean0.98263843
Minimum0
Maximum5
Zeros2420
Zeros (%)24.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:04:21.168870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.86801357
Coefficient of variation (CV)0.8833499
Kurtosis0.25804562
Mean0.98263843
Median Absolute Deviation (MAD)1
Skewness0.71429885
Sum7471
Variance0.75344755
MonotonicityNot monotonic
2023-06-26T15:04:21.267292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 3355
33.6%
0 2420
24.2%
2 1417
14.2%
3 364
 
3.6%
4 45
 
0.4%
5 2
 
< 0.1%
(Missing) 2397
24.0%
ValueCountFrequency (%)
0 2420
24.2%
1 3355
33.6%
2 1417
14.2%
3 364
 
3.6%
4 45
 
0.4%
5 2
 
< 0.1%
ValueCountFrequency (%)
5 2
 
< 0.1%
4 45
 
0.4%
3 364
 
3.6%
2 1417
14.2%
1 3355
33.6%
0 2420
24.2%

W
Categorical

Distinct5
Distinct (%)0.1%
Missing2559
Missing (%)25.6%
Memory size78.2 KiB
1.0
3679 
0.0
3281 
2.0
463 
3.0
 
17
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters22323
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 3679
36.8%
0.0 3281
32.8%
2.0 463
 
4.6%
3.0 17
 
0.2%
4.0 1
 
< 0.1%
(Missing) 2559
25.6%

Length

2023-06-26T15:04:21.385880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:04:21.525692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3679
49.4%
0.0 3281
44.1%
2.0 463
 
6.2%
3.0 17
 
0.2%
4.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 10722
48.0%
. 7441
33.3%
1 3679
 
16.5%
2 463
 
2.1%
3 17
 
0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14882
66.7%
Other Punctuation 7441
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10722
72.0%
1 3679
 
24.7%
2 463
 
3.1%
3 17
 
0.1%
4 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 7441
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22323
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10722
48.0%
. 7441
33.3%
1 3679
 
16.5%
2 463
 
2.1%
3 17
 
0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22323
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10722
48.0%
. 7441
33.3%
1 3679
 
16.5%
2 463
 
2.1%
3 17
 
0.1%
4 1
 
< 0.1%

C
Real number (ℝ)

MISSING  ZEROS 

Distinct19
Distinct (%)0.2%
Missing2200
Missing (%)22.0%
Infinite0
Infinite (%)0.0%
Mean3.745
Minimum0
Maximum18
Zeros684
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:04:21.656903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile9
Maximum18
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9068499
Coefficient of variation (CV)0.7761949
Kurtosis0.83057139
Mean3.745
Median Absolute Deviation (MAD)2
Skewness0.98037699
Sum29211
Variance8.4497763
MonotonicityNot monotonic
2023-06-26T15:04:21.791315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 1333
13.3%
2 1201
12.0%
3 1051
10.5%
4 858
 
8.6%
5 772
 
7.7%
0 684
 
6.8%
6 578
 
5.8%
7 426
 
4.3%
8 331
 
3.3%
9 198
 
2.0%
Other values (9) 368
 
3.7%
(Missing) 2200
22.0%
ValueCountFrequency (%)
0 684
6.8%
1 1333
13.3%
2 1201
12.0%
3 1051
10.5%
4 858
8.6%
5 772
7.7%
6 578
5.8%
7 426
 
4.3%
8 331
 
3.3%
9 198
 
2.0%
ValueCountFrequency (%)
18 2
 
< 0.1%
17 1
 
< 0.1%
16 5
 
0.1%
15 12
 
0.1%
14 22
 
0.2%
13 29
 
0.3%
12 47
 
0.5%
11 89
0.9%
10 161
1.6%
9 198
2.0%

E
Categorical

Distinct4
Distinct (%)0.1%
Missing2556
Missing (%)25.6%
Memory size78.2 KiB
1.0
3556 
0.0
3453 
2.0
426 
3.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters22332
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 3556
35.6%
0.0 3453
34.5%
2.0 426
 
4.3%
3.0 9
 
0.1%
(Missing) 2556
25.6%

Length

2023-06-26T15:04:21.953616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:04:22.141018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3556
47.8%
0.0 3453
46.4%
2.0 426
 
5.7%
3.0 9
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 10897
48.8%
. 7444
33.3%
1 3556
 
15.9%
2 426
 
1.9%
3 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14888
66.7%
Other Punctuation 7444
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10897
73.2%
1 3556
 
23.9%
2 426
 
2.9%
3 9
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 7444
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22332
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10897
48.8%
. 7444
33.3%
1 3556
 
15.9%
2 426
 
1.9%
3 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22332
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10897
48.8%
. 7444
33.3%
1 3556
 
15.9%
2 426
 
1.9%
3 9
 
< 0.1%

H
Categorical

Distinct2
Distinct (%)< 0.1%
Missing2627
Missing (%)26.3%
Memory size78.2 KiB
0.0
6534 
1.0
839 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters22119
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6534
65.3%
1.0 839
 
8.4%
(Missing) 2627
26.3%

Length

2023-06-26T15:04:22.322606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:04:22.563134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6534
88.6%
1.0 839
 
11.4%

Most occurring characters

ValueCountFrequency (%)
0 13907
62.9%
. 7373
33.3%
1 839
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14746
66.7%
Other Punctuation 7373
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13907
94.3%
1 839
 
5.7%
Other Punctuation
ValueCountFrequency (%)
. 7373
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13907
62.9%
. 7373
33.3%
1 839
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13907
62.9%
. 7373
33.3%
1 839
 
3.8%

I
Real number (ℝ)

MISSING  ZEROS 

Distinct44
Distinct (%)0.4%
Missing200
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean9.5538776
Minimum0
Maximum43
Zeros347
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:04:22.762196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median8
Q314
95-th percentile24
Maximum43
Range43
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.2745045
Coefficient of variation (CV)0.76141906
Kurtosis0.72299336
Mean9.5538776
Median Absolute Deviation (MAD)5
Skewness0.97366781
Sum93628
Variance52.918415
MonotonicityNot monotonic
2023-06-26T15:04:22.996360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
2 657
 
6.6%
1 642
 
6.4%
3 637
 
6.4%
4 623
 
6.2%
5 605
 
6.0%
7 573
 
5.7%
6 570
 
5.7%
9 479
 
4.8%
10 478
 
4.8%
8 469
 
4.7%
Other values (34) 4067
40.7%
ValueCountFrequency (%)
0 347
3.5%
1 642
6.4%
2 657
6.6%
3 637
6.4%
4 623
6.2%
5 605
6.0%
6 570
5.7%
7 573
5.7%
8 469
4.7%
9 479
4.8%
ValueCountFrequency (%)
43 2
 
< 0.1%
42 1
 
< 0.1%
41 1
 
< 0.1%
40 2
 
< 0.1%
39 1
 
< 0.1%
38 7
 
0.1%
37 4
 
< 0.1%
36 11
0.1%
35 6
 
0.1%
34 21
0.2%

M
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)0.1%
Missing1916
Missing (%)19.2%
Infinite0
Infinite (%)0.0%
Mean1.4152647
Minimum0
Maximum6
Zeros1858
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:04:23.154244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1734915
Coefficient of variation (CV)0.8291675
Kurtosis0.58505335
Mean1.4152647
Median Absolute Deviation (MAD)1
Skewness0.8439657
Sum11441
Variance1.3770823
MonotonicityNot monotonic
2023-06-26T15:04:23.326927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 2971
29.7%
2 1893
18.9%
0 1858
18.6%
3 907
 
9.1%
4 337
 
3.4%
5 93
 
0.9%
6 25
 
0.2%
(Missing) 1916
19.2%
ValueCountFrequency (%)
0 1858
18.6%
1 2971
29.7%
2 1893
18.9%
3 907
 
9.1%
4 337
 
3.4%
5 93
 
0.9%
6 25
 
0.2%
ValueCountFrequency (%)
6 25
 
0.2%
5 93
 
0.9%
4 337
 
3.4%
3 907
 
9.1%
2 1893
18.9%
1 2971
29.7%
0 1858
18.6%

R
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing2619
Missing (%)26.2%
Memory size78.2 KiB
0.0
7381 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters22143
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7381
73.8%
(Missing) 2619
 
26.2%

Length

2023-06-26T15:04:23.549819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:04:23.795794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7381
100.0%

Most occurring characters

ValueCountFrequency (%)
0 14762
66.7%
. 7381
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14762
66.7%
Other Punctuation 7381
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14762
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7381
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22143
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14762
66.7%
. 7381
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22143
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14762
66.7%
. 7381
33.3%

S
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

T
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing2625
Missing (%)26.2%
Memory size78.2 KiB
0.0
7088 
1.0
 
287

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters22125
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7088
70.9%
1.0 287
 
2.9%
(Missing) 2625
 
26.2%

Length

2023-06-26T15:04:23.973645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:04:24.520103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7088
96.1%
1.0 287
 
3.9%

Most occurring characters

ValueCountFrequency (%)
0 14463
65.4%
. 7375
33.3%
1 287
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14750
66.7%
Other Punctuation 7375
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14463
98.1%
1 287
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 7375
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22125
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14463
65.4%
. 7375
33.3%
1 287
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22125
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14463
65.4%
. 7375
33.3%
1 287
 
1.3%

G
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct7
Distinct (%)0.1%
Missing2589
Missing (%)25.9%
Infinite0
Infinite (%)0.0%
Mean1.2291189
Minimum0
Maximum7
Zeros1875
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:04:24.672939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0157303
Coefficient of variation (CV)0.82638896
Kurtosis0.35139395
Mean1.2291189
Median Absolute Deviation (MAD)1
Skewness0.73759687
Sum9109
Variance1.031708
MonotonicityNot monotonic
2023-06-26T15:04:24.880305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 3018
30.2%
0 1875
18.8%
2 1674
16.7%
3 657
 
6.6%
4 165
 
1.7%
5 21
 
0.2%
7 1
 
< 0.1%
(Missing) 2589
25.9%
ValueCountFrequency (%)
0 1875
18.8%
1 3018
30.2%
2 1674
16.7%
3 657
 
6.6%
4 165
 
1.7%
5 21
 
0.2%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
5 21
 
0.2%
4 165
 
1.7%
3 657
 
6.6%
2 1674
16.7%
1 3018
30.2%
0 1875
18.8%

N
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing2620
Missing (%)26.2%
Memory size78.2 KiB
0.0
7349 
1.0
 
31

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters22140
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7349
73.5%
1.0 31
 
0.3%
(Missing) 2620
 
26.2%

Length

2023-06-26T15:04:25.273463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:04:25.587200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7349
99.6%
1.0 31
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 14729
66.5%
. 7380
33.3%
1 31
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14760
66.7%
Other Punctuation 7380
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14729
99.8%
1 31
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 7380
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14729
66.5%
. 7380
33.3%
1 31
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14729
66.5%
. 7380
33.3%
1 31
 
0.1%

O
Real number (ℝ)

MISSING  ZEROS 

Distinct30
Distinct (%)0.4%
Missing1865
Missing (%)18.6%
Infinite0
Infinite (%)0.0%
Mean5.8240934
Minimum0
Maximum35
Zeros464
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:04:25.778017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile15
Maximum35
Range35
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.5380569
Coefficient of variation (CV)0.77918684
Kurtosis1.1610494
Mean5.8240934
Median Absolute Deviation (MAD)3
Skewness1.0669503
Sum47379
Variance20.593961
MonotonicityNot monotonic
2023-06-26T15:04:25.961290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 887
8.9%
2 880
8.8%
3 818
8.2%
4 814
8.1%
5 693
 
6.9%
6 587
 
5.9%
7 510
 
5.1%
0 464
 
4.6%
8 459
 
4.6%
9 417
 
4.2%
Other values (20) 1606
16.1%
(Missing) 1865
18.6%
ValueCountFrequency (%)
0 464
4.6%
1 887
8.9%
2 880
8.8%
3 818
8.2%
4 814
8.1%
5 693
6.9%
6 587
5.9%
7 510
5.1%
8 459
4.6%
9 417
4.2%
ValueCountFrequency (%)
35 1
 
< 0.1%
28 2
 
< 0.1%
27 3
 
< 0.1%
26 1
 
< 0.1%
25 2
 
< 0.1%
24 6
 
0.1%
23 8
 
0.1%
22 13
0.1%
21 17
0.2%
20 30
0.3%

U
Categorical

Distinct4
Distinct (%)0.1%
Missing2473
Missing (%)24.7%
Memory size78.2 KiB
0.0
3815 
1.0
3417 
2.0
 
290
3.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters22581
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 3815
38.1%
1.0 3417
34.2%
2.0 290
 
2.9%
3.0 5
 
0.1%
(Missing) 2473
24.7%

Length

2023-06-26T15:04:26.179643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:04:26.388772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3815
50.7%
1.0 3417
45.4%
2.0 290
 
3.9%
3.0 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 11342
50.2%
. 7527
33.3%
1 3417
 
15.1%
2 290
 
1.3%
3 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15054
66.7%
Other Punctuation 7527
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11342
75.3%
1 3417
 
22.7%
2 290
 
1.9%
3 5
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 7527
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22581
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11342
50.2%
. 7527
33.3%
1 3417
 
15.1%
2 290
 
1.3%
3 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22581
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11342
50.2%
. 7527
33.3%
1 3417
 
15.1%
2 290
 
1.3%
3 5
 
< 0.1%

D
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct20
Distinct (%)0.3%
Missing2611
Missing (%)26.1%
Infinite0
Infinite (%)0.0%
Mean4.2063879
Minimum0
Maximum19
Zeros534
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:04:26.538219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile10
Maximum19
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0585395
Coefficient of variation (CV)0.72711781
Kurtosis0.38508106
Mean4.2063879
Median Absolute Deviation (MAD)2
Skewness0.82000686
Sum31081
Variance9.3546641
MonotonicityNot monotonic
2023-06-26T15:04:26.690463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 1045
10.4%
2 984
 
9.8%
3 967
 
9.7%
4 884
 
8.8%
5 749
 
7.5%
6 650
 
6.5%
0 534
 
5.3%
7 458
 
4.6%
8 371
 
3.7%
9 263
 
2.6%
Other values (10) 484
 
4.8%
(Missing) 2611
26.1%
ValueCountFrequency (%)
0 534
5.3%
1 1045
10.4%
2 984
9.8%
3 967
9.7%
4 884
8.8%
5 749
7.5%
6 650
6.5%
7 458
4.6%
8 371
 
3.7%
9 263
 
2.6%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 2
 
< 0.1%
17 2
 
< 0.1%
16 4
 
< 0.1%
15 15
 
0.1%
14 21
 
0.2%
13 33
 
0.3%
12 83
0.8%
11 118
1.2%
10 205
2.1%

X
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)0.1%
Missing1938
Missing (%)19.4%
Infinite0
Infinite (%)0.0%
Mean1.9310345
Minimum0
Maximum10
Zeros1424
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:04:26.889812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5663649
Coefficient of variation (CV)0.81115325
Kurtosis0.64147261
Mean1.9310345
Median Absolute Deviation (MAD)1
Skewness0.90776097
Sum15568
Variance2.453499
MonotonicityNot monotonic
2023-06-26T15:04:27.118141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 2361
23.6%
2 1806
18.1%
0 1424
14.2%
3 1218
12.2%
4 648
 
6.5%
5 371
 
3.7%
6 165
 
1.7%
7 54
 
0.5%
8 10
 
0.1%
9 4
 
< 0.1%
(Missing) 1938
19.4%
ValueCountFrequency (%)
0 1424
14.2%
1 2361
23.6%
2 1806
18.1%
3 1218
12.2%
4 648
 
6.5%
5 371
 
3.7%
6 165
 
1.7%
7 54
 
0.5%
8 10
 
0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 4
 
< 0.1%
8 10
 
0.1%
7 54
 
0.5%
6 165
 
1.7%
5 371
 
3.7%
4 648
 
6.5%
3 1218
12.2%
2 1806
18.1%
1 2361
23.6%

Y
Real number (ℝ)

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2964
Minimum0
Maximum19
Zeros951
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:04:27.303859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile8
Maximum19
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5447976
Coefficient of variation (CV)0.77199295
Kurtosis0.82570367
Mean3.2964
Median Absolute Deviation (MAD)2
Skewness0.96422067
Sum32964
Variance6.4759946
MonotonicityNot monotonic
2023-06-26T15:04:27.475007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 1932
19.3%
2 1714
17.1%
3 1464
14.6%
4 1131
11.3%
0 951
9.5%
5 926
9.3%
6 680
 
6.8%
7 477
 
4.8%
8 324
 
3.2%
9 178
 
1.8%
Other values (8) 223
 
2.2%
ValueCountFrequency (%)
0 951
9.5%
1 1932
19.3%
2 1714
17.1%
3 1464
14.6%
4 1131
11.3%
5 926
9.3%
6 680
 
6.8%
7 477
 
4.8%
8 324
 
3.2%
9 178
 
1.8%
ValueCountFrequency (%)
19 1
 
< 0.1%
16 2
 
< 0.1%
15 3
 
< 0.1%
14 5
 
0.1%
13 14
 
0.1%
12 24
 
0.2%
11 69
 
0.7%
10 105
 
1.1%
9 178
1.8%
8 324
3.2%

Interactions

2023-06-26T15:04:14.279676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:45.207647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-06-26T15:03:57.944635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-06-26T15:04:07.205528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:09.107311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:11.507071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:13.852677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:16.180389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:47.102263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:49.431110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:51.443774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:53.442116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:55.533304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:57.689789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:59.594138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:01.511502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:03.348458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:05.421932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:07.338175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:09.243323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:11.686538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:13.988266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:16.320612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:47.230520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:49.574573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:51.571273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:53.565273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:55.661200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:57.811608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:03:59.714191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:01.625703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:03.467486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:05.539406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:07.453840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:09.402200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:11.875444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:04:14.136284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-26T15:04:27.712373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
EstabTermlySessionsPossible/\BLVCIMGODXYGenderEnrolStatusNCyearActualJPWEHTNU
Estab1.0000.1400.1300.134-0.028-0.0230.0080.0180.0110.0200.0330.032-0.052-0.0290.0190.0320.0240.0660.0000.0200.0330.0070.0440.0070.0000.024
TermlySessionsPossible0.1401.0000.3560.397-0.1150.0240.036-0.0920.0320.020-0.021-0.114-0.374-0.269-0.2070.0000.0910.0300.0290.0070.0310.0190.0230.0000.0000.000
/0.1300.3561.0000.878-0.104-0.1510.105-0.151-0.073-0.025-0.031-0.242-0.201-0.0340.0980.0170.0480.0470.0260.0410.0000.0570.0000.0470.0000.084
\0.1340.3970.8781.000-0.093-0.0380.063-0.161-0.155-0.021-0.035-0.217-0.192-0.0500.0470.0130.0640.0370.0240.0180.0350.0320.0000.0610.0000.040
B-0.028-0.115-0.104-0.0931.0000.2050.2260.4490.1280.3060.5210.4460.6820.3290.1100.0310.0000.0000.1560.1570.3440.2840.3520.2840.0850.266
L-0.0230.024-0.151-0.0380.2051.0000.0630.1750.2310.2220.1890.3240.1580.1900.1670.0000.0000.0520.0510.0440.1090.1450.0910.0800.0300.182
V0.0080.0360.1050.0630.2260.0631.0000.1370.0150.1460.1790.1350.1940.1130.0530.0240.0000.0300.0470.1050.1200.0900.1170.0800.0260.089
C0.018-0.092-0.151-0.1610.4490.1750.1371.0000.1210.2390.3120.3820.4400.2060.0720.0000.0090.0470.0920.0830.2030.1960.1900.1420.0000.160
I0.0110.032-0.073-0.1550.1280.2310.0150.1211.0000.1820.0970.2020.1010.1350.1090.0130.0170.0160.0430.0000.0580.0690.0490.0390.0360.084
M0.0200.020-0.025-0.0210.3060.2220.1460.2390.1821.0000.2460.2760.2640.1710.0210.0420.0280.0300.0960.0850.1830.1550.1390.1130.0240.154
G0.033-0.021-0.031-0.0350.5210.1890.1790.3120.0970.2461.0000.3360.4750.2390.0850.0000.0000.0000.1070.1410.2560.2210.2590.2140.0570.197
O0.032-0.114-0.242-0.2170.4460.3240.1350.3820.2020.2760.3361.0000.3920.2470.1190.0000.0520.0260.1180.0630.1880.2230.1890.1760.0460.297
D-0.052-0.374-0.201-0.1920.6820.1580.1940.4400.1010.2640.4750.3921.0000.3030.0890.0280.1610.0330.1370.1430.3110.2630.3200.2290.0680.227
X-0.029-0.269-0.034-0.0500.3290.1900.1130.2060.1350.1710.2390.2470.3031.0000.1690.0220.0000.0000.0920.0530.1470.1490.1380.1310.0730.128
Y0.019-0.2070.0980.0470.1100.1670.0530.0720.1090.0210.0850.1190.0890.1691.0000.0150.0000.0040.0570.0530.0270.0640.0250.0240.0000.045
Gender0.0320.0000.0170.0130.0310.0000.0240.0000.0130.0420.0000.0000.0280.0220.0151.0000.0000.0220.0000.0200.0000.0290.0200.0190.0040.006
EnrolStatus0.0240.0910.0480.0640.0000.0000.0000.0090.0170.0280.0000.0520.1610.0000.0000.0001.0000.0260.0090.0000.0000.0160.0000.0000.0000.000
NCyearActual0.0660.0300.0470.0370.0000.0520.0300.0470.0160.0300.0000.0260.0330.0000.0040.0220.0261.0000.0630.0000.0100.0000.0000.0000.0000.037
J0.0000.0290.0260.0240.1560.0510.0470.0920.0430.0960.1070.1180.1370.0920.0570.0000.0090.0631.0000.0550.1460.1020.1060.0850.0000.120
P0.0200.0070.0410.0180.1570.0440.1050.0830.0000.0850.1410.0630.1430.0530.0530.0200.0000.0000.0551.0000.1490.1250.1660.0970.0370.114
W0.0330.0310.0000.0350.3440.1090.1200.2030.0580.1830.2560.1880.3110.1470.0270.0000.0000.0100.1460.1491.0000.2380.2970.2330.0710.224
E0.0070.0190.0570.0320.2840.1450.0900.1960.0690.1550.2210.2230.2630.1490.0640.0290.0160.0000.1020.1250.2381.0000.2640.1880.0440.237
H0.0440.0230.0000.0000.3520.0910.1170.1900.0490.1390.2590.1890.3200.1380.0250.0200.0000.0000.1060.1660.2970.2641.0000.2470.0590.222
T0.0070.0000.0470.0610.2840.0800.0800.1420.0390.1130.2140.1760.2290.1310.0240.0190.0000.0000.0850.0970.2330.1880.2471.0000.0460.178
N0.0000.0000.0000.0000.0850.0300.0260.0000.0360.0240.0570.0460.0680.0730.0000.0040.0000.0000.0000.0370.0710.0440.0590.0461.0000.035
U0.0240.0000.0840.0400.2660.1820.0890.1600.0840.1540.1970.2970.2270.1280.0450.0060.0000.0370.1200.1140.2240.2370.2220.1780.0351.000

Missing values

2023-06-26T15:04:16.596263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-26T15:04:17.138674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-26T15:04:17.745638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

EstabUPNSurnameForenameMiddlenamesPreferredSurnameFormerSurnameGenderDoBEnrolStatusEntryDateNCyearActualTermlySessionsPossible/\BJLPVWCEHIMRSTGNOUDXY
0161510819c5d8d-a652-4a62-9eac-9fe483ddbca8NaNNaNNaNNaNNaNFNaNLeaver2019-09-04 00:00:007118.03335NaNNaN6.0NaN0.0NaN9.0NaNNaN17.02.0NaNNaNNaNNaNNaN19.0NaNNaNNaN1
11506846318ab5a-2d2c-42ce-ab68-0945162d683dNaNNaNNaNNaNNaNFNaNC2021-09-03 00:00:007120.066570.00.01.00.01.00.00.00.00.07.01.00.0NaN0.01.00.02.00.02.03.03
2258745eb1edd42-97c1-4a8d-b546-f49372eabdb8NaNNaNNaNNaNNaNMNaNC2019-09-04 00:00:007120.043481.00.08.00.00.00.03.00.00.013.01.00.0NaN0.01.00.03.00.05.01.04
32299203064a34b-8961-4ac6-8540-a346ee12fb8bNaNNaNNaNNaNNaNMNaNC2021-09-03 00:00:009114.035442.00.013.00.02.01.03.01.00.06.02.00.0NaN0.02.00.05.01.08.03.011
4128143de67351f-dd82-428f-8095-ba0f5a3ac738NaNNaNNaNNaNNaNFNaNC2017-09-06 00:00:007117.042502.00.06.01.01.01.05.01.00.07.01.00.0NaN0.01.00.04.00.03.03.01
51911309d4fcc5a-aa2f-4aa9-a081-6fe4a4fe7de4NaNNaNNaNNaNNaNFNaNC2020-09-03 00:00:007105.037381.00.02.00.00.00.04.00.00.01.02.00.0NaN0.01.00.05.00.03.01.01
6184759a18a4e15-7719-49a5-badc-779f1be4aa6fNaNNaNNaNNaNNaNMNaNC2020-09-02 00:00:0010117.055521.00.03.01.00.00.02.00.00.08.02.00.0NaN0.00.00.05.00.01.01.05
71952655c181651-6c0a-4b2d-b91f-25cc926c3e73NaNNaNNaNNaNNaNMNaNC2019-09-04 00:00:007126.069622.00.03.00.02.01.04.00.00.02.01.00.0NaN0.00.00.03.00.01.03.05
8168715f8b9f84f-4864-47b8-bf8f-d9fe5e133ac7NaNNaNNaNNaNNaNFNaNC2021-06-07 00:00:007116.044420.00.01.01.01.00.00.00.00.02.01.00.0NaN0.00.00.02.00.01.02.01
917613518a2235f-9bce-4e51-9fbb-d630da74e0c9NaNNaNNaNNaNNaNFNaNC2018-09-06 00:00:001098.04646NaN0.04.02.01.0NaNNaNNaNNaN9.00.0NaNNaNNaNNaNNaNNaNNaNNaN2.07
EstabUPNSurnameForenameMiddlenamesPreferredSurnameFormerSurnameGenderDoBEnrolStatusEntryDateNCyearActualTermlySessionsPossible/\BJLPVWCEHIMRSTGNOUDXY
99901672921d3316bd-95f1-4049-875a-b3c681e7a151NaNNaNNaNNaNNaNFNaNC2018-09-07 00:00:0011119.049483.00.03.01.01.01.08.01.00.06.02.00.0NaN0.01.00.04.01.08.01.02
9991173175b52bef10-e225-4985-8345-184506bb7e8cNaNNaNNaNNaNNaNFNaNC2021-09-01 00:00:009122.073623.00.06.01.01.01.0NaN0.01.09.03.00.0NaN0.02.00.03.01.05.01.04
9992219663b6a8922a-90ac-40cd-9f73-88735e59ee17NaNNaNNaNNaNNaNFNaNC2017-09-06 00:00:0011127.035362.00.012.01.00.00.05.01.00.03.00.00.0NaN0.00.00.04.01.02.04.04
9993242430a154abc5-7e8f-4a4c-8eed-cad91e2975d5NaNNaNNaNNaNNaNFNaNC2017-09-01 00:00:007120.034312.00.00.01.00.00.05.00.00.07.01.00.0NaN0.01.00.09.01.03.00.01
999419616280fcd390-27cd-4a83-8dea-4eb8b0ef1494NaNNaNNaNNaNNaNMNaNC2018-09-06 00:00:007124.05052NaNNaNNaNNaNNaNNaNNaNNaNNaN21.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2
999513087041110120-22a3-4350-adce-7435ed425ba5NaNNaNNaNNaNNaNMNaNC2018-09-25 00:00:0011121.055572.00.05.01.01.02.06.01.00.01.02.00.0NaN0.01.00.010.00.05.03.03
99961694965c969010-a33c-4db7-b061-b60984f798f5NaNNaNNaNNaNNaNFNaNC2020-09-03 00:00:0010104.055532.00.09.00.00.01.0NaN1.01.017.03.00.0NaN0.02.00.08.00.07.03.03
99971779929e344d35-36ca-40b9-99c5-112f64b0206dNaNNaNNaNNaNNaNMNaNC2018-09-07 00:00:0011125.055543.00.0NaN1.03.01.03.00.00.09.02.00.0NaN0.02.00.06.01.05.03.03
9998211438fb668a2a-f6a2-4fcf-ab4e-b2909c850516NaNNaNNaNNaNNaNMNaNC2017-09-06 00:00:009116.036360.00.03.00.00.00.01.00.00.01.00.00.0NaN0.00.00.08.00.00.00.05
9999204136ffb2b9a6-eea7-4003-aeee-e9501ef609fcNaNNaNNaNNaNNaNMNaNC2021-09-03 00:00:007117.06959NaNNaN3.0NaNNaNNaNNaNNaNNaN5.0NaNNaNNaNNaNNaNNaN12.0NaNNaNNaN5